Abstract
The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.
Highlights
Electrocardiography (ECG) is a procedure used to evaluate the electrical activity of the heart with reference to time by insertion of electrodes on the skin
Heart signals consist of several features such as P waves, QRS complex, and T waves, and studying such features plays an imperative part in the diagnosis of various arrhythmias [1]
There are other arrhythmias that are emphasized in this research, such as ventricular tachycardia, atrial flutter, atrial fibrillation, malignant ventricular, and ventricular bigeminy with the help of deep neural network algorithms
Summary
Electrocardiography (ECG) is a procedure used to evaluate the electrical activity of the heart with reference to time by insertion of electrodes on the skin. Two PhysioBank datasets (normal sinus rhythm database (NSR-DB) and MIT/BIH arrhythmia database) were used to distinguish normal and abnormal ECG signals, for which the multilayer-perceptron technique was used Another algorithm uses a four-layer of convolution neural network (CNN) to detect various arrhythmias in arbitrary length ECG dataset features. The dataset that was used in this study contains various cardiac diseases, such as arrhythmia, normal sinus, second degree AV block, first degree AV block, atrial flutter, atrial fibrillation, malignant ventricular, ventricular tachycardia, and ventricular bigeminy. IInn tthhiiss ccaassee,, tthheerree aarree ffoouurr hhiiddddeenn llaayyeerrss bbeettwweeeenn tthhee iinnppuutt aanndd oouuttppuutt llaayyeerrss [[1177]]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
